1. Field of the Invention
The invention relates to biomagnetic measurements and especially magnetoencephalographic (MEG) and electroencephalographic (EEG) measurements.
2. Description of the Related Art
It is possible to measure biomagnetic, that is, neural fields originating in brain tissue with magnetoencephalographic (MEG) measurement devices. Ionic currents which flow in the dendrites of neurons, induce a detectable magnetic field. There is a need for extremely sensitive detecting devices such as SQUIDs (Superconducting Quantum Interference Devices) because the biomagnetic signals are very small (in the order of femtoteslas) in amplitude in the MEG measurements. Therefore, those signals are very easily buried under external interference signals which typically have much higher amplitudes.
Other brain activity measurement techniques include electroencephalography (EEG), where the potential differences between different parts of the brain are measured. Electrodes are placed on the surface of the head, and the amplitude and the duration of the voltage differences between the electrodes are changing according to brain activity such as according to the state of consciousness.
Prior art publication US 2006031038 discloses a so-called SSS method (Signal Space Separation) where the measured biomagnetic signal can be divided into sums of signal components which originate in different volumes. This method can be used for eliminating interferences because the method separates biomagnetic signals from external interferences based merely on the basic physics of electromagnetic fields (that is, Maxwell's equations) and on the geometry used in the measurement.
Explaining the SSS method in a more thorough manner, a magnetic field measured by a multi-channel MEG device is analysed by examining three different volumes of the measurement geometry. The interesting source is in measurement volume V1 and the sensors are in measurement volume V2 outside volume V1. The sources of magnetic interferences and the compensation actuators are outside the aforementioned volumes in volume V3. In this examination, the V3 can also be infinite in volume. In the method, the magnetic field produced by the interesting sources disposed in volume V1 is parametrised in volume V2 as a sum of elementary fields, each of them being irrotational, sourceless and finite outside volume V1 so that a presentation of a desired accuracy is achieved for the parametrised magnetic field in volume V2. Similarly, the sum magnetic field produced by the interference fields and compensation actuators disposed in volume V3 is parameterised in volume V2 as a sum of elementary fields. The measuring device's signal vectors corresponding to each elementary field are calculated. If a magnetic signal is measured using sensors, then thereafter, the fields produced from sources disposed in different volumes can be separated by calculating the components of the measured signal vector in the basis formed by the signal vectors associated with the elementary fields.
As another application using MEG measurements, FI 20050445 discloses a method for interference suppression. In this case the source of interference is located e.g. in a patient's head or neck where it is close to the source of biomagnetic signals. In FI 20050445, two different series expansions are calculated from the measured signals. These two series expansions relate to the sources in the measurement area and to the sources outside that area. By identifying components which are present in both series expansions, the interference sources originating in the direct vicinity of the human brain, can be identified and suppressed.
The core problem in magnetoencephalography is a so-called inverse problem where the current source locations are to be estimated based on the measured magnetic fields outside the object. This is usually a rather tricky problem because in principle there is not a unique solution to the inverse problem. Solution of current source distributions can be attempted by using, e.g., minimum (Lp) norm estimates. The problem can also be constrained by using anatomical and physiological information.
A lead field of a given sensor in this context is a vector field in the source space to which the given sensor is most sensitive. Generally, in all non-parametric approaches to the source localization problem, the measured multichannel signal vector (signal values from different channels achieved by the physical sensors) is expressed as a product of a lead field matrix and a dipole moment vector, concatenated from 1-n current dipoles. The lead field matrix contains the lead fields of the physical sensors and the dipole moment vector corresponds to dipole moments to be estimated at selected points within the brain volume.
One prior art method for source localization in MEG measurements is described in Uutela et al.: “Visualization of magnetoencephalographic data using minimum current estimates”, NeuroImage 10, 173-180, 1999 and Huang et al.: “Vector-based spatial-temporal minimum L1-norm solution for MEG”, NeuroImage 31, 1025-37, 2006. It is a lead-field based solution where the minimum L1-norm solution selects the source configuration that minimizes the absolute value of the source strength and which can handle highly correlated sources.
Several other prior art methods are based on minimum L2-norm estimation which maximizes the smoothness of the solution. They also include beamformer approaches where source covariances estimated from the data are applied for focusing the solution at a selected point and at the same time reducing contributions from other source locations. In prior art publication ‘Shannon: “Communication in the Presence of Noise”, Proceedings of the IEEE, Vol. 86, No. 2, February 1998’, a widely used Shannon's theory of communication has been presented. From there, a concept of total information can be derived. It has been used in theoretical calculations for capacities of multichannel SQUID arrays. Such channel capacity calculations have been performed e.g. in a publication ‘Kemppainen, Ilmoniemi: “Channel capacity of multichannel magnetometers”, Advances in Biomagnetism, Plenum Press, New York’, where the total information per sample is obtained from:
                              I          tot                =                              1            2                    ⁢                                    ∑                              i                =                1                            N                        ⁢                                                  ⁢                                          log                2                            ⁡                              (                                                      SNR                    i                                    +                  1                                )                                                                        (        1        )            
where the SNR's are taken from orthogonalized channels (1, . . . , N).
Nenonen et al.: “Total Information of Multichannel MEG Sensor Arrays”, Proc. Biomag 2004, pp. 630-631, discloses one way of calculating total information for multichannel sensor arrays used in MEG. Nenonen examines the optimal number of channels needed with a thin-film triple-sensor array placed around the patient's head on a helmet-shaped surface. Nenonen shows that the total information associated with the sensor array does not grow after a certain number of used channels. With triple-sensors the optimal value is approximately 320 channels while with magnetometers or axial gradiometers the optimal channel amount is approximately 250 channels.
In the prior art one problem is that though the quality of the measured data has been improved by suppressing interferences, the actual measurement data is still included in the same amount of measurement channels as before the signal processing. Therefore the handling of the data can be rather demanding computationally.
Another problem in prior art has been that the geometry associated with the measurement setup (the physical sensors, the apparatus and the measurable objects) has been maintained in the cases where some processing or transformation has been made to the MEG data.
Concerning the calculation complexity of prior art solutions, forward modeling of signals which correspond to the physical sensors require calculation of the magnetic flux through pick-up loops. This is accomplished by estimating the surface integral with a certain number of integration points for approximating the non-zero area of the loop. If the number of channels is N and the number of integration points is p, there are N*p calculations required for each current dipole. This often makes the lead field matrix calculation and the dipole fitting a computationally heavy procedure. Additionally because of the non-orthogonality or spatial overlapping of the lead fields, the lead field matrix is known to be ill-posed. As a consequence, e.g. minimum L1- and L2-norm solutions require numerical regularisation with suitable regularisation parameters. The correct selection of these parameters is generally difficult to be achieved. As a result, incorrect regularisation may lead to biased source reconstruction and false data analysis.